An exploded view paradigm to disambiguate scatterplots

Small multiples is a popular visualization technique for dealing with overdraw in multi-class data. Small multiples are great at showing pieces of data individually, however, they do not explain how the different pieces fit together. They can also be difficult to understand for unacquainted users. We propose an interactive technique which uses the paradigm of exploded views to make small multiples visualizations more intelligible for unacquainted users. An exploded view is a drawing in which the different components of the object are separated by distance in such a way that the relationship between these components becomes apparent and hidden components of the data are revealed. We use the exploded view paradigm to create various animation designs for multi-class data. The designs are then compared using the Elo ranking scheme. We hypothesize that the exploded view animations increase the ability of users to appreciate the relations among data clusters (in the compound view) and at the same time get a clearer idea about the features of the individual data clusters (in the exploded view). We conduct a user study to compare this interactive approach with a compound view and an animated small multiples visualization.